Thoughtware

Article 12/01/2017

Diagnosing Your Payments: What’s Driving Your Value-Based Reimbursement?

Doctor and patient shaking hands

Presentations and discussions centered on the payment shift from fee-for-service (FFS) to value-based reimbursement (VBR) are ubiquitous. Hospitals have significant experience dealing with a variety of alphabet soup VBR programs imposed by the Centers for Medicare & Medicaid Services (CMS) and gradually adopted by the commercial payor markets, e.g., Hospital Value-Based Purchasing, Hospital Readmission Reduction, Hospital-Acquired Condition Reduction, etc. New payment models whereby health care providers share in the cost savings or losses for managing quality outcomes and patient care utilization, i.e., Accountable Care Organizations (ACO), bundled payments, etc., also are on the rise. Therefore, it’s no surprise the CMS may reach its goal of linking 90 percent of FFS payments to quality and alternative payment models1 in 2018.

Clinical documentation and diagnosis coding are playing increasingly important roles in the success of hospital participants under these VBR models, and health care finance professionals should be well aware of their effect on payments. Diagnosis coding is critical to properly identify patients with chronic conditions due to the potential for higher use of services and effects on hospital quality and cost performance in an Alternative Payment Model (APM) if not managed correctly. Accurate clinical documentation and coding are important for patients in an APM given their roles in calculating a risk adjustment factor (RAF). The overall RAF of a population under a VBR model could determine whether you incur savings or pay for a loss to the CMS.

What’s the HCC RAF?

Implemented in 2004, Hierarchical Condition Category (HCC) coding is the Medicare risk adjustment methodology developed for Medicare Advantage Plans and now used in various VBR models. The methodology uses patient demographics and hierarchical disease conditions (diagnoses containing a risk factor) to calculate an overall RAF score to reflect the expected complexity of treating patients with chronic conditions. While the methodology also takes into consideration other factors, the important point is the higher a patient’s RAF score, the higher assumed complexity (and associated cost) of treating that patient.

Exhibit 1 below is an illustrative example of an HCC’s presence or omission on a patient’s overall RAF score. In this scenario, the differential codes result in the patient RAF score falling from 2.12 to 0.65, a fairly significant drop. Read on to see how this can significantly affect your payments.

Exhibit 1

Diagnosing Your Payments: What’s Driving Your Value-Based Reimbursement? Exhibit 1

HCC RAF Effect on Payments

Care Management Fee Effects

Risk adjustment methodologies in VBR models can help keep providers that treat complex patients from being unfairly penalized for the additional treatment costs. The Comprehensive Primary Care Plus (CPC+) program is a great example of demonstrating how an HCC score can alter payment levels. In CPC+, the CMS pays a monthly care management fee (CMF) for each beneficiary attributed to a participating primary care provider. The payment amount for each beneficiary is based on the expected care complexity, as reflected in the HCC score, which is assigned to a risk tier defined by Medicare. The higher the HCC score, the higher the risk tier and corresponding payment (per attributed beneficiary per month in this model). Columns (d) and (f) in Exhibit 2 (illustrative), which aggregate CMF payments by risk tier, demonstrate the potential effect on a CPC+ practice’s overall CMF revenue by assignment into higher-risk tiers.

Exhibit 2

Diagnosing Your Payments: What’s Driving Your Value-Based Reimbursement? Exhibit 2

Accountable Care Organization Effect

The CMS also uses the HCC RAF to adjust ACO expenditure benchmarks under the Medicare Shared Savings Program. For program participants, a higher RAF translates to a higher-expenditure benchmark. Exhibit 3 demonstrates how the change in risk adjustment to the ACO benchmark can affect an ACO’s savings or losses. Keeping the baseline versus actual cost outcomes constant across the three scenarios, we can see how a slight shift of the RAF score up or down—column (c)—can have million-dollar implications—column (e)—and determine whether the ACO experiences a savings or loss.

Exhibit 3

Diagnosing Your Payments: What’s Driving Your Value-Based Reimbursement? Exhibit 3

Quick Note on MACRA

The CMS has proposed HCCs will become a bigger factor in calculating a physician’s overall Merit-based Incentive Payment System (MIPS) score in 2018. Essentially, the CMS will compare the severity of physicians’ attributed patient populations and award additional points to those who treat patients with a higher severity of illness. This could potentially mean the difference between a positive or negative payment adjustment on Medicare Part B FFS revenue under MIPS.

Next Steps

Evaluate Current State

Health care financial professionals should begin evaluating the current state of their HCC coding capture processes and identifying gaps in capturing accurate diagnoses for patients seen in the hospital (inpatient and outpatient) or physician practices. An assessment can help develop an understanding of where the organization needs to improve and what process improvements may be necessary to accurately capture HCC coding information.

Engage Providers on HCCs

Physicians and mid-level providers may be interested in understanding how risk adjustment works under these new payment models. HCC stratification provides physicians with the opportunity to demonstrate the severity burden of their patient panels and can help inform previsit planning for those patients with multiple chronic conditions. In many cases, opportunities to improve diagnosis coding in physician practices exist, whether by helping providers understand the importance of diagnosis coding or assessing if the appropriate level of clinical documentation exists to justify an HCC diagnosis code.

U.S. Department of Health and Human Services, 2015